principal component analysis

(1.9 hours to learn)


Principal component analysis is a method for projecting data into a lower dimensional space. It works by finding the space which maximizes the variance of the projections, or equivalently, minimizes the reconstruction error. Mathematically, it corresponds to computing the SVD of the transformed data, or the spectral decomposition of the covariance matrix.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Machine Learning (2013)
An online machine learning course aimed at a broad audience.
Location: Lecture sequence "Dimensionality reduction"
Author: Andrew Y. Ng
Other notes:
  • Click on "Preview" to see the videos.


Supplemental resources (the following are optional, but you may find them useful)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman
Additional dependencies:
  • Lagrange multipliers

See also